The Basics

In this lesson, we outline how to structure manager 1:1s so they become one of the most powerful tools for growth, clarity, and alignment in the IC–EM relationship.

- We highlight that the gold standard for 1:1s is weekly, recurring, and 30 minutes long, which is long enough for meaningful depth but manageable on a manager’s packed calendar.
- We stress that cancellations should be rare because these meetings are the “lifeblood” of the relationship and the primary space where trust, alignment, and problem-solving happen.
- We recommend keeping a running meeting notes document with agendas, discussion points, and assigned action items to ensure continuity and accountability.
- We emphasize that although 1:1s take only \~1–2% of our weekly hours, they influence 30–70% of how quickly we grow and how effectively we leverage our EM.
- We reinforce that strong engineers prepare intentionally for these meetings—coming with notes, plans, and reflection—rather than waiting passively for their manager to drive the conversation.

Understanding The Role

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This part of the course explains why becoming an engineering manager (EM) is not just a promotion—it’s a **career pivot**. Understanding the differences in scope and responsibilities between individual contributors and managers is essential to succeed in EM interviews and in the role itself.

- **Role Shift:** Transitioning from engineer to EM means moving from primarily technical tasks to handling broader responsibilities involving people and strategy.
- **Scope vs. Focus:** Senior engineers focus on deep technical problems, while EMs begin to balance both technical and organizational concerns; directors and VPs go even further toward people/strategy.
- **Career Tracks Diverge:** Staying on the IC track keeps you focused on technology, but switching to EM roles sets you on a path toward executive leadership (e.g., director, VP).
- **Lack of Preparation:** Most engineers aren’t trained to manage people or lead strategically, which causes difficulties when transitioning into EM roles.
- **Wrong Reasons:** Some pursue management purely to climb the ladder, which can result in disengaged or ineffective leaders who use their teams as stepping stones.

Hi, I’m Uzair

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

Uzair is an experienced engineering manager with a passion for helping other leaders grow and succeed in their careers. With a solid background in software development and extensive hiring experience, he brings deep insight into the engineering management interview process.

- Currently serves as an engineering manager at Stripe, with previous experience at Amazon
- Has over 10 years of engineering management experience across tech companies and consulting firms, and more than 20 years in software development overall
- Former Amazon bar raiser, responsible for upholding high hiring standards across 250+ interviews conducted at Amazon and over 750 total in his career
- Has trained, managed, and promoted more than 15 engineering managers
- Brings a wealth of knowledge in both leadership development and technical interviewing to support aspiring engineering managers

Round Structure ML Algorithm

In this section, we dive into what makes the **ML algorithm coding interview** distinct from traditional coding rounds. It’s a focused, high-pressure test of both our theoretical depth and practical coding ability.

- We’ll be asked to **implement ML algorithms from scratch**—no scikit-learn, no TensorFlow—just raw code.
- This round tests both our **understanding of the algorithm’s inner workings** and our ability to **code it cleanly and efficiently**.
- With only **45–60 minutes**, we’re expected to focus on **core logic and code structure**, not perfection or full library-level implementations.
- The scope is typically **limited to common algorithms** like linear regression, logistic regression, or k-means—so it's highly trainable.
- With preparation, we can excel by knowing the **key steps, math, and edge cases** behind each algorithm and practicing how to express them in code.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

[Paper Reading] Netflix : Query Facet Mapping and its Applications in Streaming Services

Led by [Dharin Shah](https://www.linkedin.com/in/dharinshah/)

For a Taro Premium discount from Dharin, use his referral link here: <https://www.jointaro.com/r/dharins694/>

This deep-dive goes through how Netflix built their internal query to facet mapping layer to have better understand intent of the query. For example - matching 'anim' to 'Demon Slayer' as higher rank than perhaps 'animal planet'. In this session we dive into the white paper released by Netflix around their implementation.

Link to Paper: [**https://dl.acm.org/doi/pdf/10.1145/3477495.3536330**](https://dl.acm.org/doi/pdf/10.1145/3477495.3536330)

How To Debug Effectively with AI As My Partner?

In this lesson we talk about how to debug effectively with AI as a partner during a coding interview, and when to use it versus when to rely on your own judgment.

- **Use AI to generate more test cases, but step through code yourself** — AI can rapidly produce test cases that would take much longer to write manually, but since it predicts words rather than executing code, it can reproduce the same bugs it originally introduced, making it unreliable for tracing logic line by line.
- **Avoid leaning on AI for debugging, but use it strategically when stuck** — rather than asking AI to find or fix a bug directly, prompt it to brainstorm multiple possible causes, which keeps you in the driver's seat, surfaces hypotheses for you to validate with your own judgment, and reduces the risk of confidently wrong AI suggestions.
- **Always rerun all tests after a fix, not just the failing one** — thorough test coverage lets passing tests serve as reliable verification; if your test suite is thin, compensate with more manual code inspection to avoid introducing regressions you won't have time to track down later.

Underrated Career Goals

We highlight the qualities engineers consistently underestimate and why these matter far more than most people realize when choosing a company or team.

Here are the core points from the lesson:

- We emphasize that the most underrated factor by far is a truly supportive team: a strong manager, teammates who challenge and care about us, and an environment where we grow—something incredibly rare, worth cherishing, and often more valuable than joining a prestigious brand.
- We explain that growth opportunities don’t come automatically from prestige; even at places like Meta or Google, you can land on a team with poor scope or weak culture, and real growth often requires intentional team selection and proactive effort.
- We stress that stability and sustainable work-life balance are often undervalued by early-career engineers, who assume they can power through long hours, even though sustained 60-hour weeks degrade learning, productivity, and mental health—and that the best creators historically do only a few hours of deep work daily.

Promotion Dialog Examples

In this lesson, we examine how *not* to ask for a promotion, then show how to reframe the conversation in a way that aligns incentives, builds trust, and opens up productive dialogue with our EM.

- We highlight that asking “Can I get promoted?” is a poor approach because it’s self-focused, lacks evidence, feels transactional, and corners the manager into a yes/no response.
- We break down why this framing fails: it signals entitlement, doesn’t show impact, ignores team goals, and provides no data or context for advocacy.
- We offer a better alternative: expressing commitment to the team, desire to increase impact, and asking what steps are needed to reach the next level—an open-ended, collaborative question.
- We explain that managers value signals of long-term commitment and team-oriented motivation, not demands based on tenure or frustration.
- We reinforce that smart framing aligns our goals with the manager’s incentives: helping us grow increases team impact, which helps them succeed too.

Maximize Your Equity And Benefits In Tech [Part 22] - Healthcare And Miscellaneous Benefits

From picking the right medical plan to using hidden financial perks, there’s a lot you can do to stretch your benefits beyond equity. Here are the core points from the lesson:

- PPO, EPO, and HMO plans vary in flexibility and cost, with PPOs offering the most freedom and HMOs the lowest cost but most restrictions
- Understanding plan terms like premiums, deductibles, copays, and coinsurance helps you estimate total medical costs more accurately
- HSAs offer exceptional tax advantages and long-term growth potential, especially if you invest the funds and save receipts for future reimbursement
- FSAs are employer-based, use-it-or-lose-it accounts that are best used only when you know you’ll spend the funds during the year
- Tech workers may also have access to other valuable benefits like legal services, fertility support, wellness stipends, and insurance—but employer-provided life and disability coverage may not be enough if you truly need it

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Maximize Your Equity And Benefits In Tech [Part 21] - Bottom Line

There are a few essential things to keep in mind when managing retirement accounts. Here are the core points from the lesson:

- 401(k)s are effective for automating savings and deferring taxes, especially in high tax brackets, and it's important to capture the employer match and invest contributions
- Pre-tax vs. Roth decisions aren’t one-size-fits-all, but choosing wisely can reduce your lifetime tax bill with the help of smart withdrawal strategies
- Mega backdoor Roths allow large tax-free contributions if your plan supports them, while backdoor Roths offer smaller contributions with more complexity and caution required

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Become An Engineer Influencer [Part 23] - Coaching

Become An Engineer Influencer [Part 15] - Headline/Preview Testing

Model Training

In this segment, we cover **model training**, a phase where many candidates fall into autopilot. To stand out, we need to show thoughtfulness, adaptability, and a clear connection between our choices and the problem at hand.

- **Mediocre performance** involves picking a default model (e.g., logistic regression or random forest), using out-of-the-box settings with no tuning or justification.
- **Strong candidates** choose models deliberately, explaining how their selection fits the **data characteristics, business goals, or system constraints**.
- We improve performance through **hyperparameter tuning**, using techniques like grid search or informed manual adjustments based on EDA insights.
- When appropriate, we apply **ensemble methods** (e.g., boosting, bagging, stacking) to push performance further.
- Most importantly, we demonstrate that our modeling choices are **intentional and grounded in context**, not just rote or random experimentation.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Having The Meeting

In this lesson, we walk through exactly how to run a high-impact 1:1 with our EM by using the time intentionally to build trust, surface wins, work through challenges, and create momentum through clear action items.

- We start each meeting with 2–5 minutes of personal check-in to strengthen rapport, since life and work affect each other deeply.
- We share weekly wins to stay visible and ensure our contributions are recognized—while also making future performance review writing much easier.
- We move through our pre-seeded agenda topics (e.g., promotion planning, feedback digestion, team conflicts), which flow smoothly because we prepared them ahead of time.
- We close with clear action items for both sides—then begin the next week’s 1:1 by reviewing whether the previous actions were completed.
- We reinforce that framing wins with a “so what?” (impact or next step) and writing notes live during the meeting both strengthen trust and make the manager feel valued.

Partner, Not A Boss

In this lesson, we talk about shifting the mindset from seeing an engineering manager as an authority figure to treating them as a partner whose incentives align closely with ours, unlocking far more growth and support.

- We emphasize that while EMs have real power (performance reviews, promotions, PIPs), relating to them through fear undermines trust, collaboration, and growth.
- We highlight that both we and our EM share the same fundamental goal: maximizing our impact, because our increased impact directly elevates their team impact.
- We explain that when we grow, deliver more, and navigate the org effectively, our manager looks better—so supporting our development is in their best interest.
- We encourage adopting a peer-like, partnership mentality rather than a hierarchical one, which creates more openness, trust, and meaningful dialogue.
- We reinforce the idea that adding value to our manager—through communication, initiative, and impact—encourages them to reciprocate with resources, advocacy, and opportunities.

Be Upfront About Your Goals

In this lesson, we emphasize the importance of sharing our performance goals openly with our EM so they can effectively advocate for us during performance reviews, which are human-driven, subjective, and highly dependent on manager alignment.

- We highlight that performance ratings are not generated by an objective system (i.e. they’re decided by people), so managers need clear insight into what rating we’re aiming for to advocate properly.
- We stress that “no negative feedback” does *not* mean we’re on track for a good rating or promotion; assuming this is one of the most common and costly mistakes ICs make.
- We reinforce that 1:1s are the central place to share our target rating or promotion timeline, whether we’re aiming for “meets,” “exceeds,” or a promotion, so we can then build a plan with our EM.
- We note that even if our goal is simply to maintain solid performance (e.g., a 3/5), we should still say so explicitly to avoid slipping into a low rating unexpectedly.
- We share a story illustrating how unchecked assumptions about performance can lead to disappointment—and how early, honest conversations could provide clarity and opportunities to improve.

So What Do Engineering Managers Do?

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

An engineering manager’s role extends far beyond attending meetings or writing reports—it centers on one core responsibility: *Ensuring the team delivers*. This requires balancing accountability, leadership, and adaptability across multiple domains.

- **Facilitate Decision-Making and Own Processes:** EMs don’t always make every decision, but they must facilitate the decision-making process and ensure team adherence to defined practices like quality standards or definitions of done.
- **Own Delivery Outcomes:** Regardless of who does what on the team, the EM is ultimately responsible for all deliverables—both successes and failures.
- **Bridge Business and Engineering:** EMs connect the technical team with the broader business, helping engineers understand customer needs, product lifecycles, and why building the *right* thing is more important than just building it right.
- **Ensure Product Integrity and Fill Gaps:** Maintaining consistent product quality across releases is part of the EM's responsibility. They must also step into roles as needed—whether that’s scrum master, product owner, or project manager—to remove blockers and enable progress.
- **Represent the Team and Grow Talent:** EMs act as the external face of the team to stakeholders and leadership while also coaching and developing individual engineers to help them grow in their careers.

System Design Example: TikTok Recommendation System

Here’s a summary of the **infrastructure-focused ML system design** walkthrough. We are building a robust, scalable recommendation platform to support multiple product teams, focusing on infrastructure needs such as latency, reliability, and modularity.

- **We design for scalability and flexibility**, supporting 10M+ daily predictions across various surfaces (e.g., homepage, search, email), with &lt;50ms latency for real-time use cases and batch support for offline use.
- **We architect the system** with modular components: batch and streaming feature pipelines, a centralized feature store, a model training platform, a model registry, and scalable inference servers, all integrated with monitoring and alerting.
- **We implement tiered feature computation** (long-term, medium-term, real-time) using tools like Spark and Flink, and store user, item, and contextual features consistently to power accurate and fresh predictions.
- **We support multiple modeling strategies**, including two-tower models, matrix factorization, and DNNs, and use a two-stage architecture with candidate generation followed by precise ranking.
- **We deploy models reliably** using CI/CD pipelines, blue-green deployments, and shadow testing, and maintain performance through A/B testing, autoscaling servers, inference optimizations, and drift detection.

This approach ensures we build ML systems that are production-ready, highly available, and adaptable to the evolving needs of diverse teams.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Grow Fundamental Skills

We explain why growing fundamental, transferable skills is one of the most powerful ways to reduce layoff risk and strengthen long-term career resilience. Here are the core points from the lesson:

- There is far more to software engineering than coding, especially at scale, where communication, project management, leadership, and relationship-building determine whether work actually ships.
- Over-indexing on a specific technology, language, or product is risky, because teams, systems, and entire products can disappear, while transferable skills allow you to move across teams or companies.
- Engineers who can clearly design systems, think through edge cases, write thoughtful plans, and communicate tradeoffs create outsized impact compared to those who jump straight into code.
- So-called “soft skills” are actually the highest-leverage skills, because they compound across roles, companies, and industries, while technical tools and frameworks inevitably change.
- Focusing your time on high-leverage problems and influence, rather than low-level tooling details, is what drives career growth, adaptability, and durability during periods of layoffs.

Project Presentation

Some companies include a **project presentation round** in their ML interview process, where we’re expected to walk interviewers through a real project from start to finish. This is a powerful opportunity to showcase our end-to-end technical skills, communication ability, and strategic thinking.

- **Use visuals** to anchor the conversation—simple diagrams in Excalidraw or Google Docs help explain architecture clearly and keep interviewers engaged.
- **Explain concepts like you’re teaching a junior engineer**, avoiding jargon and offering quick context before diving into complex ideas (e.g., why negative sampling is needed).
- **Stay composed during interruptions**, as these are often tests of our communication clarity rather than signs of concern.
- **Show real trade-off thinking**—don’t just name-drop methods; explain *why* one approach was selected over others based on constraints like latency or scalability.

This round is about more than technical depth—it’s about demonstrating that we can **lead projects, make informed decisions, and communicate them with confidence and clarity**.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.